mmdetection/tests/test_models/test_dense_heads/test_anchor_head.py

# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase

import torch
from mmengine import Config
from mmengine.structures import InstanceData

from mmdet import *  # noqa
from mmdet.models.dense_heads import AnchorHead


class TestAnchorHead(TestCase):

    def test_anchor_head_loss(self):
        """Tests anchor head loss when truth is empty and non-empty."""
        s = 256
        img_metas = [{
            'img_shape': (s, s, 3),
            'pad_shape': (s, s, 3),
            'scale_factor': 1,
        }]

        cfg = Config(
            dict(
                assigner=dict(
                    type='MaxIoUAssigner',
                    pos_iou_thr=0.7,
                    neg_iou_thr=0.3,
                    min_pos_iou=0.3,
                    match_low_quality=True,
                    ignore_iof_thr=-1),
                sampler=dict(
                    type='RandomSampler',
                    num=256,
                    pos_fraction=0.5,
                    neg_pos_ub=-1,
                    add_gt_as_proposals=False),
                allowed_border=0,
                pos_weight=-1,
                debug=False))
        anchor_head = AnchorHead(num_classes=4, in_channels=1, train_cfg=cfg)

        # Anchor head expects a multiple levels of features per image
        feats = (
            torch.rand(1, 1, s // (2**(i + 2)), s // (2**(i + 2)))
            for i in range(len(anchor_head.prior_generator.strides)))
        cls_scores, bbox_preds = anchor_head.forward(feats)

        # Test that empty ground truth encourages the network to
        # predict background
        gt_instances = InstanceData()
        gt_instances.bboxes = torch.empty((0, 4))
        gt_instances.labels = torch.LongTensor([])

        empty_gt_losses = anchor_head.loss_by_feat(cls_scores, bbox_preds,
                                                   [gt_instances], img_metas)
        # When there is no truth, the cls loss should be nonzero but
        # there should be no box loss.
        empty_cls_loss = sum(empty_gt_losses['loss_cls'])
        empty_box_loss = sum(empty_gt_losses['loss_bbox'])
        assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
        assert empty_box_loss.item() == 0, (
            'there should be no box loss when there are no true boxes')

        # When truth is non-empty then both cls and box loss
        # should be nonzero for random inputs
        gt_instances = InstanceData()
        gt_instances.bboxes = torch.Tensor(
            [[23.6667, 23.8757, 238.6326, 151.8874]])
        gt_instances.labels = torch.LongTensor([2])

        one_gt_losses = anchor_head.loss_by_feat(cls_scores, bbox_preds,
                                                 [gt_instances], img_metas)
        onegt_cls_loss = sum(one_gt_losses['loss_cls'])
        onegt_box_loss = sum(one_gt_losses['loss_bbox'])
        assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
        assert onegt_box_loss.item() > 0, 'box loss should be non-zero'